Paper Info
Title
A Blockchain Based Federated Learning for Message Dissemination in Vehicular Networks
Abstract
Message exchange among vehicles plays an important role in ensuring road safety. Emergency message dissemination is usually carried out by broadcasting. However, high vehicle density and mobility lead to challenges in message dissemination such as broadcasting storm and low probability of packet reception. This paper proposes a federated learning based blockchain-assisted message dissemination solution. Similar to the incentive-based Proof-of-Work consensus in blockchain, vehicles compete to become a relay node (miner) by processing the proposed Proof-of-Federated-Learning (PoFL) consensus which is embedded in the smart contract of blockchain. Both theoretical and practical analysis of the proposed solution are provided. Specifically, the proposed blockchain based federated learning results in more vehicles uploading their models in a given time, which can potentially lead to a more accurate model in less time as compared to the same solution without using blockchain. It also outperforms other blockchain approaches in reducing 65.2% of time delay in consensus, improving at least 8.2% message delivery rate and preserving privacy of neighbor vehicle more efficiently. The economic model to incentivize vehicles participating in federated learning and message dissemination is further analyzed using Stackelberg game. The analysis of asymptotic complexity proves PoFL as the most scalable solution compared to other consensus algorithms in vehicular networks.
Year
DOI
Venue
2022
10.1109/TVT.2021.3132226
IEEE Transactions on Vehicular Technology
Keywords
DocType
Volume
Blockchain,federated learning,smart contract
Journal
71
Issue
ISSN
Citations 
2
0018-9545
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Ferheen Ayaz142.12
Zhengguo Sheng244640.43
Daxin Tian320432.49
Yong Liang Guan42037163.66